1. Field of the Invention
The present invention relates to process modeling and monitoring, especially using complex or periodic signals. Moreover, the invention relates to complex signal decomposition, modeling and classification for use in monitoring the operational state of any machine, process or signal. More particularly, a complex signal can be classified using the present invention for health monitoring or for information rectification.
2. Description of the Related Art
Well known conventional modeling and analysis methods use several sensors measuring operational system parameters to monitor equipment or processes—generically “systems”. The related signals from sensors can be observed directly to understand how the system is functioning. Alternatively, for unattended operation, methods are well known for comparing real-time sensor signals against stored or predetermined thresholds in an automated fashion. When the signals exceed those thresholds, an exception condition or an alarm is generated, thus requiring human intervention only when a sensor datum value exceeds a corresponding threshold. Such methods focus on using the instantaneous value of sensors and other parameters to describe the current state of a system, but do not take advantage of time-domain information locked up in the sensor signals. It would be useful to take advantage of such time-domain information to better monitor the system, and even determine what operational state (among many acceptable states) the system is in.
In the field of vibration analysis, methods are known for examining the power spectral density function from an accelerometer or acoustic pickup to provide means for monitoring rotating or cyclic equipment. Typically, frequencies of interest are examined, and thresholds (lower or upper limit) are placed on the power level expected for these frequencies. If a threshold is pierced, this is indicative of an unsatisfactory operating condition or a developing problem. A great deal of work is involved in identifying the frequencies of interest and expected power levels for each particular piece of equipment that is monitored in this fashion. Problem diagnosis is also typically very specific to the kinds of indications presented with the appearance of the particular problem, and must be worked out specifically for each machine. It would be useful to have an empirical data-driven way of determining the health or the operational state of a machine based on one or more vibration or acoustic signals.
In a different technical area, digital data transmission is frequently accomplished—whether over a cable (e.g. Cat. 5, coaxial cable, etc.) or through radio transmission (e.g. broadcast, digital telecommunication, an IEEE 802.11b interface)—by modulation of an analog carrier signal. Further, to improve data transmission rates, the data being transmitted is compressed and encoded onto the transmission signal carrier, typically as sinusoidal waves encoding binary data in the phase and amplitude of the wave. Presently, well-known data encoding and transmission techniques include quadrature amplitude modulation (QAM) and discrete multitone (DMT). Well-known methods for extracting such encoded data include frequency filtering, signal decomposition and wavelet analysis.
However, during transmission these types of signals can suffer from attenuation and interference due to noise or transmission media deterioration, for example. In some cases, noise and signal degradation is sufficient to all but obliterate the original transmitted signal, making it impossible to extract the data encoded therein using prior art techniques. Accordingly, when noise or degradation is high, it would be useful to be able to reconstruct a meaningful signal from the noisy and/or attenuated signal that is being received. This essentially amounts to determining which of a finite set of datagrams an attenuated signal most closely resembles. There is a need for a signal analysis method that may be applied to a single complex signal to extract an original signal.
One empirical model-based monitoring technique known in the art is described in U.S. Pat. No. 5,764,509 to Gross et al., the teachings of which are incorporated herein by reference. In this technique, multiple sensor signals measuring physically correlated parameters are modeled in an empirical technique to provide estimates of those values. Discrepancies between the estimates and the actual values from the sensors indicate a developing process or machine failure, or sensor failure. The model generates the estimates using a reference library of selected historic snapshots of sensor values representative of known operational states. However, the described embodiments therein do not utilize the time domain information in the sensor signals, and instead usually treat the data in distinct and disconnected time-contemporaneous snapshots. It would be useful to provide the kind of empirical modeling of Gross et al. for use with time domain information. What is needed is a way of using a complex signal as an input to a multivariate modeling system such as that of Gross et al.
Where time domain information is locked up in one or more sensor or parameter signals detected from an instrumented process or machine, what is needed is a way to model the process or machine with the time-domain signal for one or more acceptable and identifiable states of operation, and to do so without investing a great deal of time and effort in coming up with first-principles equations that approximate those states. What is further needed is a way to categorize or classify system operational states based on a complex signal.